Turning Data into Knowledge - Data-Led Catalyst Optimisation

有机金属催化剂的使用在工业上已经得到了很好的证实，这在很大程度上是因为它们具有高选择性和催化活性。然而，过渡金属催化反应的机理在不同的底物上应用时往往会发生变化，从而导致产率和选择性的下降。在使用新的底物时，通常需要对催化剂进行优化，这可能是一项耗时和耗费资源的工作。因此，越来越多的人正在从寻找特权催化剂转向将其无处不在地应用于单个催化过程，而是转向预测给定底物组的最佳条件和催化剂设计的能力。为此，布里斯托尔的研究人员开发了一系列配体知识库(LKB)；用于有机金属催化的配体数据库，以及描述每个配体的空间和电子性质的计算计算描述符。利用主成分分析(PCA)，可以根据配体结构性质的相似性对其进行分组，并可以对其催化性能进行预测。在与拜耳的合作下，将对化学领域的配体进行筛选，以了解其对各种底物的催化能力。这种高通量筛选方法将提供关于配体空间的哪些部分与给定应用的最佳性能相关的信息，以及这些作为底物因素的变化情况。通过主成分分析，可以从它们的描述符中识别出对给定过程有效的催化剂的特征，并将其用作新配体的设计原则。然后将合成新的配体，以判断这种方法在优化催化剂发现方面的可行性。此外，任何新的配体都将被添加到LKB中，并将研究有效配体的催化机理。混合配基体系的潜在用途也将作为进一步加强催化剂优化过程的一种方法进行研究。

The use of organometallic catalysts is well established in industry, due in large part to their high selectivity and catalytic activity. However, the mechanism of transition metal catalytic reactions is often subject to change when applied to different substrates, which can lead to decreases in yield and selectivity. Often, catalyst optimisation will be necessary when using new substrates, which can be a time and resource consuming endeavour. As such, there is a growing drive to shift away from the search for a privileged catalyst to apply ubiquitously to an individual catalytic process, and instead towards the ability to predict the optimal conditions and catalyst design for a given set of substrates. To this end, researches in Bristol have developed a range of Ligand Knowledge Bases (LKBs); databases of ligands for organometallic catalysis, and computationally calculated descriptors that describe the steric and electronic properties of each ligand. Using Principle Component Analysis (PCA), ligands can be grouped based on similarities in their structural properties, and predictions on their catalytic properties can be made. In collaboration with Bayer, ligands from across the chemical space will be screened for their catalytic capabilities towards a variety of substrates. This high-throughput screening approach will give information on what sections of the ligand space correlated with the best performance for a given application, and how those change as a factor of substrate. Using PCA, the features of effective catalysts for a given process can be identified from their descriptors and used as design principles for new ligands. New ligands will then be synthesised to judge the viability of this approach in the optimisation of catalyst discovery. In addition, any new ligands will be added to the LKB, and the catalytic mechanisms of effective ligands will be investigated. The potential use of mixed ligand systems will also be investigated as a method to further enhance the process of catalyst optimisation.